
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Optional[int]    =	logging.get_logger(__name__)

lowerCamelCase_    :			List[str]    =	{
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    int          =			"""pix2struct_text_model"""
			__UpperCamelCase   :    Optional[int]          =			["""past_key_values"""]
			__UpperCamelCase   :    List[Any]          =			{
			    """hidden_size""": """hidden_size""",
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}

			def __init__(							self			:							List[str]    ,						snake_case_			:							List[Any]=5_0244    ,						snake_case_			:							Any=768    ,						snake_case_			:							str=64    ,						snake_case_			:							List[str]=2048    ,						snake_case_			:							str=12    ,						snake_case_			:							Tuple=12    ,						snake_case_			:							Optional[Any]=32    ,						snake_case_			:							int=128    ,						snake_case_			:							Dict=0.1    ,						snake_case_			:							Optional[Any]=1e-6    ,						snake_case_			:							Optional[int]=1.0    ,						snake_case_			:							Optional[int]="gelu_new"    ,						snake_case_			:							List[str]=0    ,						snake_case_			:							Optional[Any]=False    ,						snake_case_			:							List[Any]=0    ,						snake_case_			:							str=1    ,						snake_case_			:							Optional[int]=False    ,						snake_case_			:							int=True    ,						**snake_case_			:							Tuple    ,						):
							UpperCamelCase_:				Any						=    vocab_size
							UpperCamelCase_:				Any						=    hidden_size
							UpperCamelCase_:				str						=    d_kv
							UpperCamelCase_:				Tuple						=    d_ff
							UpperCamelCase_:				Union[str, Any]						=    num_layers
							UpperCamelCase_:				Optional[Any]						=    num_heads
							UpperCamelCase_:				str						=    relative_attention_num_buckets
							UpperCamelCase_:				int						=    relative_attention_max_distance
							UpperCamelCase_:				str						=    dropout_rate
							UpperCamelCase_:				List[Any]						=    layer_norm_epsilon
							UpperCamelCase_:				Any						=    initializer_factor
							UpperCamelCase_:				Optional[int]						=    use_cache

							UpperCamelCase_:				Tuple						=    eos_token_id
							UpperCamelCase_:				Any						=    decoder_start_token_id

							# for backwards compatibility
							UpperCamelCase_:				Optional[int]						=    dense_act_fn

							super().__init__(
							    pad_token_id=snake_case_    ,						eos_token_id=snake_case_    ,						decoder_start_token_id=snake_case_    ,						tie_word_embeddings=snake_case_    ,						is_decoder=snake_case_    ,						**snake_case_    ,						)




			@classmethod
			def    lowerCAmelCase__						(							cls			:							List[Any]    ,						snake_case_			:							Union[str, os.PathLike]    ,						**snake_case_			:							Union[str, Any]   ):
							cls._set_token_in_kwargs(snake_case_   )

							UpperCamelCase_, UpperCamelCase_:				str						=    cls.get_config_dict(snake_case_    ,						**snake_case_   )

							# get the text config dict if we are loading from Pix2StructConfig
							if config_dict.get("""model_type"""   ) == "pix2struct":
											UpperCamelCase_:				Tuple						=    config_dict["""text_config"""]

							if "model_type" in config_dict and hasattr(cls    ,						"""model_type"""   ) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

							return cls.from_dict(snake_case_    ,						**snake_case_   )




class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""pix2struct_vision_model"""

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							Dict=768    ,						snake_case_			:							Optional[Any]=768    ,						snake_case_			:							Dict=2048    ,						snake_case_			:							List[Any]=64    ,						snake_case_			:							List[Any]=12    ,						snake_case_			:							Optional[int]=12    ,						snake_case_			:							Optional[int]="gelu_new"    ,						snake_case_			:							List[str]=1e-6    ,						snake_case_			:							Optional[int]=0.0    ,						snake_case_			:							List[Any]=0.0    ,						snake_case_			:							Optional[int]=1e-10    ,						snake_case_			:							Optional[int]=1.0    ,						snake_case_			:							str=4096    ,						snake_case_			:							str=32    ,						snake_case_			:							Dict=128    ,						**snake_case_			:							Optional[int]    ,						):
							super().__init__(**snake_case_   )

							UpperCamelCase_:				Union[str, Any]						=    hidden_size
							UpperCamelCase_:				int						=    patch_embed_hidden_size
							UpperCamelCase_:				Dict						=    d_ff
							UpperCamelCase_:				Dict						=    dropout_rate
							UpperCamelCase_:				str						=    num_hidden_layers
							UpperCamelCase_:				Optional[Any]						=    num_attention_heads
							UpperCamelCase_:				Optional[Any]						=    initializer_range
							UpperCamelCase_:				List[Any]						=    initializer_factor
							UpperCamelCase_:				Any						=    attention_dropout
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				str						=    dense_act_fn
							UpperCamelCase_:				Tuple						=    seq_len
							UpperCamelCase_:				Optional[int]						=    relative_attention_num_buckets
							UpperCamelCase_:				Tuple						=    relative_attention_max_distance
							UpperCamelCase_:				List[Any]						=    d_kv




			@classmethod
			def    lowerCAmelCase__						(							cls			:							List[Any]    ,						snake_case_			:							Union[str, os.PathLike]    ,						**snake_case_			:							Optional[int]   ):
							cls._set_token_in_kwargs(snake_case_   )

							UpperCamelCase_, UpperCamelCase_:				int						=    cls.get_config_dict(snake_case_    ,						**snake_case_   )

							# get the vision config dict if we are loading from Pix2StructConfig
							if config_dict.get("""model_type"""   ) == "pix2struct":
											UpperCamelCase_:				Optional[int]						=    config_dict["""vision_config"""]

							if "model_type" in config_dict and hasattr(cls    ,						"""model_type"""   ) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

							return cls.from_dict(snake_case_    ,						**snake_case_   )




class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""pix2struct"""
			__UpperCamelCase   :    int          =			True

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							List[str]=None    ,						snake_case_			:							Optional[int]=None    ,						snake_case_			:							Dict=1.0    ,						snake_case_			:							Dict=0.02    ,						snake_case_			:							Optional[Any]=False    ,						snake_case_			:							Optional[int]=False    ,						snake_case_			:							Union[str, Any]=True    ,						**snake_case_			:							List[str]    ,						):
							super().__init__(tie_word_embeddings=snake_case_    ,						is_encoder_decoder=snake_case_    ,						**snake_case_   )

							if text_config is None:
											UpperCamelCase_:				int						=    {}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""   )

							if vision_config is None:
											UpperCamelCase_:				Any						=    {}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""   )

							UpperCamelCase_:				Any						=    PixaStructTextConfig(**snake_case_   )
							UpperCamelCase_:				str						=    PixaStructVisionConfig(**snake_case_   )

							UpperCamelCase_:				Union[str, Any]						=    self.text_config.decoder_start_token_id
							UpperCamelCase_:				Any						=    self.text_config.pad_token_id
							UpperCamelCase_:				Any						=    self.text_config.eos_token_id

							UpperCamelCase_:				Any						=    initializer_factor
							UpperCamelCase_:				Any						=    initializer_range

							UpperCamelCase_:				Optional[Any]						=    self.initializer_range
							UpperCamelCase_:				Any						=    self.initializer_range

							UpperCamelCase_:				int						=    is_vqa

			@classmethod
			def    lowerCAmelCase__						(							cls			:							Optional[Any]    ,						snake_case_			:							PixaStructTextConfig    ,						snake_case_			:							PixaStructVisionConfig    ,						**snake_case_			:							Optional[Any]   ):
							return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**snake_case_   )




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				List[Any]						=    copy.deepcopy(self.__dict__   )
							UpperCamelCase_:				Tuple						=    self.text_config.to_dict()
							UpperCamelCase_:				Tuple						=    self.vision_config.to_dict()
							UpperCamelCase_:				Dict						=    self.__class__.model_type
							return output








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






